Teaching MLOps in Higher Education through Project-Based Learning
Not on the map. Why?
Abstract (arXiv)
Building and maintaining production-grade ML-enabled components is a complex endeavor that goes beyond the current approach of academic education, focused on the optimization of ML model performance in the lab. In this paper, we present a project-based learning approach to teaching MLOps, focused on the demonstration and experience with emerging practices and tools to automatize the construction of ML-enabled components. We examine the design of a course based on this approach, including laboratory sessions that cover the end-to-end ML component life cycle, from model building to production deployment. Moreover, we report on preliminary results from the first edition of the course. During the present year, an updated version of the same course is being delivered in two independent universities; the related learning outcomes will be evaluated to analyze the effectiveness of project-based learning for this specific subject.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 9 of its 31 references that are in the map. First posted on arXiv 2 Feb 2023.
Builds on
9 of 31 references.
References · 9
- 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
- 2018Model Cards for Model Reportingcited by 1,068
- 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 979
- 2019Green AIcited by 620
- 2017Machine Teaching: A New Paradigm for Building Machine Learning Systemscited by 41
- 2020Teaching Software Engineering for AI-Enabled Systemscited by 7
- 2022Eliciting Best Practices for Collaboration with Computational Notebookscited by 4
- 2021Lessons Learned from Educating AI Engineerscited by 3
- 2022Pynblint: a Static Analyzer for Python Jupyter Notebookscited by 3
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 2.
3 papers.
- 2023Towards green AI-based software systems: an architecture-centric approach (GAISSA)cited by 3
- 2025Addressing Quality Challenges in Deep Learning: The Role of MLOps and Domain Knowledgecited by 0
- 2025Scaffolding Collaborative Learning in STEM: A Two-Year Evaluation of a Tool-Integrated Project-Based Methodologycited by 0
- 2025Scaffolding Collaborative Learning in STEM: A Two-Year Evaluation of a Tool-Integrated Project-Based Methodology2 Sep 2025
- 2025Addressing Quality Challenges in Deep Learning: The Role of MLOps and Domain Knowledge14 Jan 2025
- 2023Towards green AI-based software systems: an architecture-centric approach (GAISSA)19 Jul 2023